A Review of Nutrient Management Studies Involving Finger Millet in the Semi-Arid Tropics of Asia and Africa
Bibliographic record
Abstract
Finger millet (Eleusine coracana (L.) Gaertn) is a staple food crop grown by subsistence farmers in the semi-arid tropics of South Asia and Africa. It remains highly valued by traditional farmers as it is nutritious, drought tolerant, short duration, and requires low inputs. Its continued propagation may help vulnerable farmers mitigate climate change. Unfortunately, the land area cultivated with this crop has decreased, displaced by maize and rice. Reversing this trend will involve achieving higher yields, including through improvements in crop nutrition. The objective of this paper is to comprehensively review the literature concerning yield responses of finger millet to inorganic fertilizers (macronutrients and micronutrients), farmyard manure (FYM), green manures, organic by-products, and biofertilizers. The review also describes the impact of these inputs on soils, as well as the impact of diverse cropping systems and finger millet varieties, on nutrient responses. The review critically evaluates the benefits and challenges associated with integrated nutrient management, appreciating that most finger millet farmers are economically poor and primarily use farmyard manure. We conclude by identifying research gaps related to nutrient management in finger millet, and provide recommendations to increase the yield and sustainability of this crop as a guide for subsistence farmers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".